---
title: Filtering on PgVector
description: Learn how to filter knowledge base searches using Pdf documents with user-specific metadata in PgVector.
---

## Code

```python
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.utils.media import (
    SampleDataFileExtension,
    download_knowledge_filters_sample_data,
)
from agno.vectordb.pgvector import PgVector

# Download all sample CVs and get their paths
downloaded_cv_paths = download_knowledge_filters_sample_data(
    num_files=5, file_extension=SampleDataFileExtension.PDF
)

# Initialize PgVector
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

vector_db = PgVector(table_name="recipes", db_url=db_url)

# Step 1: Initialize knowledge with documents and metadata
# ------------------------------------------------------------------------------
# When initializing the knowledge, we can attach metadata that will be used for filtering
# This metadata can include user IDs, document types, dates, or any other attributes

knowledge = Knowledge(
    name="PgVector Knowledge Base",
    description="A knowledge base for PgVector",
    vector_db=vector_db,
)

knowledge.add_contents(
    [
        {
            "path": downloaded_cv_paths[0],
            "metadata": {
                "user_id": "jordan_mitchell",
                "document_type": "cv",
                "year": 2025,
            },
        },
        {
            "path": downloaded_cv_paths[1],
            "metadata": {
                "user_id": "taylor_brooks",
                "document_type": "cv",
                "year": 2025,
            },
        },
        {
            "path": downloaded_cv_paths[2],
            "metadata": {
                "user_id": "morgan_lee",
                "document_type": "cv",
                "year": 2025,
            },
        },
        {
            "path": downloaded_cv_paths[3],
            "metadata": {
                "user_id": "casey_jordan",
                "document_type": "cv",
                "year": 2025,
            },
        },
        {
            "path": downloaded_cv_paths[4],
            "metadata": {
                "user_id": "alex_rivera",
                "document_type": "cv",
                "year": 2025,
            },
        },
    ]
)

# Step 2: Query the knowledge base with different filter combinations
# ------------------------------------------------------------------------------

agent = Agent(
    knowledge=knowledge,
    search_knowledge=True,
)

agent.print_response(
    "Tell me about Jordan Mitchell's experience and skills",
    knowledge_filters={"user_id": "jordan_mitchell"},
    markdown=True,
)

```

## Usage

<Steps>
  <Step title="Install libraries">
    ```bash
    pip install -U agno sqlalchemy psycopg openai
    ```
  </Step>

  <Step title="Set environment variables">
    ```bash
    export OPENAI_API_KEY=xxx
    ```
  </Step>

  <Snippet file="run-pgvector-step.mdx" />

  <Step title="Run the example">
    <CodeGroup>
    ```bash Mac
    python cookbook/knowledge/filters/vector_dbs/filtering_pgvector.py
    ```

    ```bash Windows
    python cookbook/knowledge/filters/vector_dbs/filtering_pgvector.py
    ```
    </CodeGroup>
  </Step>
</Steps>